A Review on Forecasting Agricultural Demand and Supply with Crop Price Estimation Using Machine Learning Methodologies

Abstract

Agriculture plays a vital role in Indian economy. It contributes 18% of total India’s GDP. In India, most of the crops are solely dependent upon weather conditions. Hence, more yield of crops can be achieved by analyzing agro-climate data using machine learning techniques. Machine learning (ML) is a crucial perspective for acquiring real-world and operative solution for crop yield issue. From a given set of predictors, ML can predict a target/outcome by using Supervised Learning. To get the desired outputs need to generate a suitable function by set of some variables which will map the input variable to the aim output. Crop yield prediction incorporates forecasting the yield of the crop from past historical data which includes factors such as temperature, humidity, ph, rainfall, crop name. It gives us an idea for the finest predicted crop which will be cultivate in the field weather conditions. These predictions can be done by a machine learning algorithm called Random Forest. It will attain the crop prediction with best accurate value. The algorithm random forest is used to give the best crop yield model by considering least number of models. It is very useful to predict the yield of the crop in agriculture sector.